Experimental analysis of application-level intrusion detection algorithms

Authors:
Yuhong Dong;Sam Hsu;Saeed Rajput;Bing Wu
Affiliations:
Department of Advanced Technologies, Alcorn State University, Alcorn State, MS 39096, USA.;Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA.;Division of Math, Science and Technology, Nova Southeastern University, Florida 33314, USA.;Department of Mathematics and Computer Science, Fayetteville State University, Fayetteville, NC 28301, USA
Venue:
International Journal of Security and Networks
Year:
2010

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Abstract

Intrusion Detection System (IDS) plays a very important role on information security. In this paper, we present an application-level intrusion detection algorithm named Graph-based Sequence-Learning Algorithm (GSLA). GSLA includes data pre-processing, normal profile construction and session marking. In GSLA, the normal profile is built through a session-learning method, which is used to determine an anomaly session. We conduct experiments and evaluate the performance of GSLA with other conventional algorithms, such as Markov Chain Model (MM) and K-means Algorithm. The results show that GSLA improves the effectiveness of anomaly detection.